Chaotic Logistic Map Research Articles

Mobile robot path planning based on hybrid ant colony optimization

To solve the problems of the ant colony optimization (ACO), such as slow convergence speed, easy to fall into local extremum and deadlock in path planning, this paper proposed an improved ACO, which was hybridized by PSO based on logistic chaotic mapping, called hybrid ant colony optimization (HACO). According to the number of obstacles around the next feasible node, HACO distributes the initial pheromones unevenly to avoid the ant getting stuck in deadlock. According to the orientation of the next node selected by the ant, the heuristic information is adaptively adjusted to guide the ant to the direction of the target position. When updating the pheromone, the local and global search mechanism of the particle swarm optimization is used to improve the pheromone update rule and accelerate convergence speed. Finally, the grid method is used to construct the environment map, and simulation experiments are conducted in different environments. The experimental results verify the effectiveness and feasibility of the improved algorithm.

Authenticated Public Key Elliptic Curve Based on Deep Convolutional Neural Network for Cybersecurity Image Encryption Application.

The demand for cybersecurity is growing to safeguard information flow and enhance data privacy. This essay suggests a novel authenticated public key elliptic curve based on a deep convolutional neural network (APK-EC-DCNN) for cybersecurity image encryption application. The public key elliptic curve discrete logarithmic problem (EC-DLP) is used for elliptic curve Diffie-Hellman key exchange (EC-DHKE) in order to generate a shared session key, which is used as the chaotic system's beginning conditions and control parameters. In addition, the authenticity and confidentiality can be archived based on ECC to share the EC parameters between two parties by using the EC-DHKE algorithm. Moreover, the 3D Quantum Chaotic Logistic Map (3D QCLM) has an extremely chaotic behavior of the bifurcation diagram and high Lyapunov exponent, which can be used in high-level security. In addition, in order to achieve the authentication property, the secure hash function uses the output sequence of the DCNN and the output sequence of the 3D QCLM in the proposed authenticated expansion diffusion matrix (AEDM). Finally, partial frequency domain encryption (PFDE) technique is achieved by using the discrete wavelet transform in order to satisfy the robustness and fast encryption process. Simulation results and security analysis demonstrate that the proposed encryption algorithm achieved the performance of the state-of-the-art techniques in terms of quality, security, and robustness against noise- and signal-processing attacks.

Performance evaluation of a single footing over rigid inclusions-reinforced soft soil - use of a novel LWSO-RF model to predict load transfer efficiency

The rigid inclusion (RI) reinforcement technique has been increasingly used to reduce settlements for engineering constructions over soft soils. However, the load transfer mechanisms within the granular mattress are not well understood, especially for the RI-reinforced systems with the presence of structural elements such as footings. To evaluate the performance of a single footing over RI-reinforced soft soils, a novel intelligence approach combining the white shark optimizer (WSO) and the Logistic chaotic mapping (LCM) was proposed to optimize the random forest (RF) model for predicting the load transfer efficiency (LTE). Seven variables were considered to systematically investigate the LTE, including the loading magnitude (L), the load eccentricity (LE), the geometrical and strength parameters of the load transfer platform thickness (LT), Young’s modulus (E), cohesion (C) and friction angle (F)) and the compression ratio (CR). Based on the feature selection method, nine different combination models were developed to estimate the LTE. Their performances were compared using the determination coefficient (R2), the root mean squared error (RMSE), the variance accounted for (VAF) and the Willmott’s index (WI). The evaluation results indicated that the LWSO-RF model containing seven variables (L, LT, LE, E, CF, FA, CR) is the best one as it gives the highest prediction accuracy in both the training and testing phases. The sensitivity results demonstrated that LT is the most important variable for the LTE prediction. This work utilizes an intelligence approach to extend the traditional numerical simulation analysis for predicting the LTE, which provides additional guidance to further assess the performance of foundations over RI-reinforced soft soil.

Irreversibility of symbolic time series: A cautionary tale.

Many empirical time series are genuinely symbolic: Examples range from link activation patterns in network science, to DNA coding or firing patterns in neuroscience, to cryptography or combinatorics on words. In some other contexts, the underlying time series is actually real valued, and symbolization is applied subsequently, as in symbolic dynamics of chaotic systems. Among several time series quantifiers, time series irreversibility-the difference between forward and backward statistics in stationary time series-is of great relevance. However, the irreversible character of symbolized time series is not always equivalent to the one of the underlying real-valued signal, leading to some misconceptions and confusion on interpretability. Such confusion is even bigger for binary time series-a classical way to encode chaotic trajectories via symbolic dynamics. In this paper we aim to clarify some usual misconceptions and provide theoretical grounding for the practical analysis-and interpretation-of time irreversibility in symbolic time series. We outline sources of irreversibility in stationary symbolic sequences coming from frequency asymmetries of nonpalindromic pairs which we enumerate, and prove that binary time series cannot show any irreversibility based on words of length m<4, thus discussing the implications and sources of confusion. We also study irreversibility in the context of symbolic dynamics, and clarify why these can be reversible even when the underlying dynamical system is not, such as the case of the fully chaotic logistic map.

Chaotic logistic map sequences with good auto-correlation properties

In this work, chaotic sequences with good auto-correlation properties are presented. The studied sequences are synthesized by means of the logistic map function and, been chaotic but fully deterministic, become a good alternative for well-known sequences, for example the Zadoff-Chu sequences, especially for small size sequences commonly used for frame synchronization purposes in digital communications systems. A new auto-correlation goodness metric is proposed, and it is employed as objective function in order to maximize the correlation goodness for short synchronization sequences synthesized by means the logistic map function.

Hyperspectral prediction of pigment content in tomato leaves based on logistic-optimized sparrow search algorithm and back propagation neural network

Leaf pigment content can reflect the nutrient elements content of the cultivation medium indirectly. To rapidly and accurately predict the pigment content of tomato leaves, chlorophyll a, chlorophyll b, chlorophyll and carotenoid were extracted from leaves of tomato seedlings cultured at different nitrogen concentrations. The visible/near-infrared(VIS/NIR) hyperspectral imaging (HSI) non-destructive measurement technology, 430-900 nm and 950-1650 nm, with total variables of 794, was used to obtain the reflection spectra of leaves. An improved strategy of the sparrow search algorithm (SSA) based on Logistic chaotic mapping was proposed and optimized the back propagation (BP) neural network to predict the pigment content of leaves. Different pretreatment methods were used to effectively improve the prediction accuracy of the model. The results showed that when the nitrogen concentration in the nutrient solution was 302.84 mg·L-1, the pigment content of leaves reached the maximum. Meanwhile, the inhibition effect of high concentration was much stronger than that of low concentration. To address the problem that the SSA is prone to get in premature convergence due to the reduction of population diversity at the end of the iteration, the initialization of the SSA population by Logistic chaotic mapping improves the initial solution quality, convergence speed and search capacity. The root mean squared error (RMSE), coefficient of determination (R2) and relative percent deviation (RPD) of chlorophyll a were 0.77, 0.77 and 2.08, respectively. The RMSE, R2 and RPD of chlorophyll b were 0.30, 0.66 and 1.71, respectively. The RMSE, R2 and RPD of chlorophyll were 0.88, 0.81 and 2.28, respectively. The RMSE, R2 and RPD of carotenoid were 0.14, 0.75 and 2.00, respectively. The HSI technology combined with machine learning algorithms can achieve rapid and accurate prediction of crop physiological information, providing data support for the precise management of fertilization in facility agriculture, which is conducive to improving the quality and output of tomatoes.

Image encryption based on 2DNA encoding and chaotic 2D logistic map

DNA has recently been investigated as a possible medium concerning ultra-compact information storage and ultra-scale computation. The development of secure image encryption systems has recently received a certain effective and new direction from chaos-based cryptographic algorithms. This paper proposes a novel image encryption algorithm, 2DNALM, based on double-dynamic DNA sequence encryption and a chaotic 2D logistic map. The three phases regarding the suggested approach are as follows: the first phase involves permuting the positions of the pixels using a position key-based scrambling operation. The second phase involves double DNA encoding on scrambled images using various rules by DNA cryptography concept to produce an encoded image, and in the final step, an image which has been encoded is encrypted using XOR operation and chaotic keys created through a chaotic 2D logistic map. The entropy analysis and experimental findings show that the suggested scheme exhibits great encryption and withstands several common attacks.

An optimized deep learning model for optical character recognition applications

The convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recognition applications; the proposed method was evaluated for performance in terms of computational accuracy, convergence analysis, and cost.

Transformer Fault Diagnosis Based on DLH-GWO-SVM

In the field of transformer fault diagnosis, there are many diagnostic methods. These diagnostic methods are either not high accuracy or are used too long. In this paper, a dimension learning-based hunting (DLH) search strategy optimization grey wolf algorithm support vector machine (DLH-GWO-SVM) model was proposed. In the process of initializing the grey wolf population, logistic chaotic mapping was used to improve the quality of the initial wolf position. In the Wolf screening process, the DLH search strategy was adopted to solve the problem that GWO was prone to fall into the local optimal solution, and the improved Grey wolf algorithm (IGWO) was formed. Finally, the transformer fault types were classified by support vector machine (SVM), and IGWO was used to optimize the penalty factor and kernel parameter (g) of the SVM. Through the experimental analysis, the accuracy of transformer fault diagnosis can reach 95.83% after the application of the DLH-GWO-SVM model.

An Enhanced Security in Medical Image Encryption Based on Multi-level Chaotic DNA Diffusion

A novel medical image encryption technique has been proposed based on the features of DNA encodingdecoding in combination with Logistic map approach. The approach is proven for encryption of highly sensitive medical images with 100 percent integrity or negligible data loss. Testing is done on both high and low-resolution images. Proposed encryption technique consists of two levels of diffusion using the actual structure of the DNA. In the first level of diffusion process, we have used DNA encoding and decoding operations to generate DNA sequence of each pixel. The originality of the work is to use a long DNA structure stored in a text file stored on both sender and receiver’s end to improve the performance of the proposed method. In this initial level of diffusion, DNA sequences are generated for each pixe-land in each of the DNA sequence. Index values are obtained by employing a search operation on the DNA structure. This index values are further modified and ready to be used for next diffusion process. In the second level diffusion, a highly chaotic logistic map is iterated to generate sequences and is employed to extract the chaotic values to form the cipher images. The correlation coefficient analysis, Histogram analysis, Entropy analysis, NPCR, and UACI exhibit significant results. Therefore; the proposed technique can play an important role in the security of low-resolution medical images as well as other visible highly sensitive images.

Securing LSB steganography using bite substitution and image blocking

Protecting secret messages is a vital issue, in this paper’s research, a simplified, highly secure method of message steganography will be introduced. The proposed method will use a complicated PK, which contains information to select a secret block from the color image to be used as a covering block; also it will contain the values of the chaotic logistic map model to run this model to generate the indices key needed to substitute the message binary matrix. The PK will provide a huge keyspace capable to resist hacking attacks, the extracted message will be very sensitive to any minor changes in the PK, and any changes in this key during the extraction phase will be considered a hacking attempt by producing a damaged extracted message. It will be shown that the proposed method will be always efficient when changing the message length and changing the covering images. The proposed method will be implemented using various messages and various covering images, the obtained results will be analyzed using various types of data analysis methods to prove the improvements provided by the proposed method (quality, security, and efficiency).

Complex CLMM Private Keys to Secure Digital Color Images

Color digital images are one of the most widespread and widely used types of data, and the image can be secret or of a special nature, or it can be a carrier of confidential data, which makes protecting it very important. In this research, a new method for protecting color digital images will be presented, and the ease of implementing this method and its suitability for any digital image will be shown. The proposed method will provide a high degree of security and provide a high degree of protection for the digital image through the use of a complex private key based on chaotic logistic map model, six chaotic key will be used to form six integer keys, and these keys will be resized to match the dimensions of each color channel. The proposed method will use two rounds of data cryptography; each round will require three private keys, one for each color. Different variations of CLMM will be used to generate 1D and 2D chaotic keys, the results of using 1D chaotic keys will be compared with the results of using 2D chaotic keys and some recommendations will be raised. The proposed method will be implemented, different types of results analysis's (Quality analysis, sensitivity analysis, and throughput analysis) to improve the enhancement provided by yje proposed method comparing with other existing methods.

3D Content Encryption Using Multi-Level Chaotic Maps

Nowadays, 3D content is becoming an essential part of multimedia applications, when the 3D content is not protected, hackers may attack and steal it. This paper introduces a proposed scheme that provides high protection for 3D content by implementing multiple levels of security with preserving the original size using weight factor (w). First level of security is implemented by encrypting the texture map based on a 2D Logistic chaotic map. Second level is implemented by shuffling vertices (confusion) based on a 1D Tent chaotic map. Third level is implemented by modifying the vertices values (diffusion) based on a 3D Lorenz chaotic map. Results illustrate that the proposed scheme is completely deform the entire 3D content according to Hausdorff Distance (HD) approximately around 100 after the encryption process. It provides a high security against brute force attack because it has large key space equal to 10165 and secret key sensitivity using NPCR near 99:6% and UACI near 33:4%. The histogram and HD indicate the decrypted 3D content is identical to the origin where HD values approximate zero.

A novel chaos-based permutation for image encryption

Image encryption is an essential method for ensuring confidentiality during transmission on open channels. Digital images have a high correlation with large redundant data, requiring alternative spatial handling during encryption. Digital chaos offers shared properties of randomness and sensitivity that can provide secure image encryption. The security and performance of chaotic image ciphers depend on the underlying digital chaotic map. Therefore, classical chaotic maps must improve security while maintaining implementation time. The main objective of this paper is to propose a new image encryption algorithm based on an enhanced chaotic map that offers high security and low time consumption. A new perturbed logistic chaotic map based on hybridizing backward and forward perturbation methods is introduced, which exhibits better chaotic features than other existing chaotic systems such as a large chaotic range, higher sensitivity, and randomness. Based on this, a new image encryption algorithm is proposed, employing a novel permutation operation and two substitution operations to achieve superior encryption speed and efficiency. The novel permutation operation based on a chaotic data sequence utilizes all chaotic states to generate 8-bit numbers. Each index of the resulting 8-bit number is compiled into one block. The plain image is then scanned in accordance with the randomized indices of each block. The two substitution operations involve an XORing operation for each pixel and a hashing process for each block. The newly proposed encryption algorithm can effectively encrypt images of varying sizes and types, transforming them into indecipherable cipher images that successfully pass through rigorous security tests, including differential attack analysis with a score of 28/28 and local Shannon entropy with a score of 25/28. The experimental findings showcase the superior performance of the proposed cipher over existing chaotic image ciphers, creating a cipher image that resembles noise and has the ability to resist a diverse range of cyber-attacks.

Performance Evaluation of AES, ECC and Logistic Chaotic Map Algorithms in Image Encryption

I&#x0D; n these days of technology, the usage of images has become increasingly high especially now that almost everyone has access to internet. Also, image helps us to learn, grabs our attention, explains complicated concepts as well as inspires us. Sharing these images is essential and therefore image encryption algorithms are proposed to secure the transmission of these image from many types of attacks such as Man-in-the-middle attack (MITM). In this paper, we proposed a hybrid security system that consist of Elliptic Curve Cryptography (ECC) and Advanced Encryption System (AES). Where ECC is used to generate private/public keys and AES is for encryption and decryption of the image using the ECC generated keys. The system works as follows; it begins by inserting the image to be encrypted which is “lena.png” of size 256*256 and passing it to AES Algorithm along with the generated public key. Then, in AES algorithm, the public key is hashed then used to encrypt the image with AES encryption algorithm. On the other hand, the decryption algorithm works as follows; inserting the encrypted image then calculating the decryption key to use it to decrypt the image using AES decryption algorithm. Finally, our experimental results shows that the National Institute of Standards and Technology (NIST) test shows that the ECC generated keys have better randomness than using only AES generated keys. Also, the encrypted image histogram show that the image pixels values are well distributed across all three channels R, G and B. This shows that the hybrid system is a step further to get a more secure image encryption system against attacks with the generated ECC keys. To get further, Logistic chaotic map has been used to encrypt images for comparison purposes with AES and ECC generated images in terms of randomness, security and histogram

Enhanced Diffusion and Confusion Based Encryption Algorithm Using Differential Evolution and Chaotic Mapping

Encryption is critical to protecting sensitive data, especially images, against potential illegal access and breaches. The use of chaotic maps for image encryption is widespread among researchers. However, it is necessary to set up initial parameters for chaotic maps to generate random sequences. These parameters need to be selected with care. To determine the optimal initial parameters for image encryption, evolutionary optimization techniques have been used. In this paper, we propose a secure image-based communication system based on an optimized image encryption algorithm. This approach uses differential evolution optimization, which is an ideal solution for reducing convergence time when optimizing chaotic map parameters. Furthermore, the security parameters of these techniques are evaluated, including entropy, Number of Pixel Change Rate (NPCR), Unified Average Changed Intensity (UACI), and correlation coefficients. The simulation results of the proposed encryption approach by combining Arnold’s cat map, chaotic logistic map and optimization method show that the encrypted image is safe against attacks.

Simple and Efficient Chaotic Based Method of Message Cryptography

A simple and easy to implement method of secret message cryptography will be introduced. The message to be encrypted-decrypted is to be converted to binary matrix, this matrix is to be reshaped to 16 columns matrix, and the contents of this matrix will be rearranged base on the contents of the generated indices key. A chaotic paramaters included in the private key will be used to run a chaotic logistic map model to generate a 16 elements chaotic key, this key will be sorted to get the required indices key. The proposed method will be flixeble and it will be used to encrypt-decrypt any message with any length. Changing the message or/and changing the key will not affect the method. The produced decrypted message will be sensitive to the selected PK, any changes in the private key in the decryption process will produce a carrupted unreadable message. The proposed method will be tested and implemented using various in sizes messages, the obtained result will be used to prove the sensitivity, quality and efficiency of the proposed method.

Multiple Blocks, Multiple Rounds Method for Image Cryptography

In this research paper a simple, flexible, secure and efficient method of digital image cryptography will be introduced. The method will enhance some features of standard methods like DES and AES. The introduced method will divide the source data into blocks, the block size will be variable, the number of rounds will be also variable and they will be determined by the user when selecting the private key. The proposed method will use a complicated private key, which will provide a huge key space capable to resist hacking attacks, the produced decrypted data will be very sensitive to the selected private key, any minor changes in the key will be considered as a hacking attempt by producing damaged decrypted data. The private key will be used to divide the image into blocks, to set the number of rounds and to generate the required for rounds secret keys. The private key will contain the values of chaotic parameters to run two chaotic logistic map models to generate two2D chaotic keys; these keys will be converted to decimal integers to apply the required XORing operations. The proposed method will be tested and implemented using various images; the obtained results will be used to prove the enhancement provided by this method: quality of cryptography, security, speed and sensitivity.

Triple Rounds to Protect Text Files

Text file may be confidential and it can contain secret information that required protection from being hacked. In this research paper a simple method of text files cryptography will be introduced, this method will suit any text file with any size; the sequence of required operations will remain the same without any change even if we change the text file and the private key. The introduced method of data cryptography will provide a high level of security; the private key will provide a huge key space making the hacking process impossible. The private key will have a complicated structure with multiple components, these components are to be used to generate three keys needed to apply three rounds of encryption-decryption, the keys will be very sensitive to any minor changes, any changes in the private key components in the decryption phase will be considered as a hacking attempt. The private key will base on chaotic logistic map model to generate 2D chaotic keys. The proposed method will be implemented and the obtained results will be analyzed using various types of analysis to prove the efficiency enhancement provide by the proposed method.

Multiple 3D Chaotic Keys to Protect Digital Images

A simple, multipurpose, efficient, sensitive and highly qualities method of image cryptography will be introduced in this research paper. The method will be easily used to encrypt-decrypt any type of image with any size (gray and color images). The proposed method will secure the image by providing a high degree of image protection based on using a complex private key, which contains eight values, each of the has a double data type. The private key will provide the required key space to resist any hacking attack. The private key components will be used to calculate the size of the generated 3D chaotic logistic key. The private key will also be used to run two chaotic logistic map models to get two different chaotic keys base on the chaotic parameters values included in the private key. Two rounds of XORing operations will be used to generate the encrypted-decrypted image. The generated outputs of the proposed method will be sensitive to the selected value of the private key. The proposed method will be very flexible, changing the source image or/and changing the private key will not require any changes in the method algorithm. The proposed method will be tested and implemented using various image, the obtained results will be discussed to prove the features provided by the method: quality, sensitivity, security and efficiency.